检索结果-内蒙古大学图书馆

Using Apriori Algorithm on Students' Performance data for Association Rules Mining

学校读者我要写书评

暂无评论

Using Apriori Algorithm on Students' Performance Data for As...

2nd International Seminar on Education Research and Social science(ISERSS 2019)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students' achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.

关键词： data mining apriori algorithm association rules students' achievement analysis

An Energy Efficient VM Migration Algorithm in data Centers

学校读者我要写书评

暂无评论

An Energy Efficient VM Migration Algorithm in Data Centers

2017 16th International Symposium on Distributed Computing and Applications to Business,Engineering and science（DCABES 2017）

作者： Xiaodong Wu Yuzhu Zeng Guoxin Lin Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingFujian Province University

In order to reduce operation and maintenance expense and also increase the resource utilization rate,server consolidation and virtualization solutions have been adopted in modern cloud computing data ***,the scheduling policies of virtual machine（VM） migration have been regarded as an effective method for energy *** this paper,we address the problem of VM consolidation in cloud data centers.A power aware scheduling algorithm THRUG,which is combined with a utilization threshold strategy and a VM selection policy,is *** tries to select the most appropriate VMs for migration each time when a physical machine（PM） is considered as being overloaded,such that the utilization of this PM is just not more than the utilization threshold,such that both the energy consumption and the number of VM migration can be *** experimental results show that compared with other algorithm,the proposed algorithm can effectively reduce the number of VM migrations as well as the energy consumption.

关键词：

Integrating MOOCs with Traditional Classroom Instruction to Improve Teaching Effectiveness

学校读者我要写书评

暂无评论

Integrating MOOCs with Traditional Classroom Instruction to ...

2017 2nd International Seminar on Education Innovation and Economic Management(SEIEM 2017)

Massive Open Online Course(MOOC) is a new teaching mode appeared in recent years and its emergence has constituted some challenge to the traditional teaching. However, MOOCs teaching mode has both advantages and disadvantages. In this paper, both the merits and the demerits of MOOC education are analyzed. We point out that the MOOC education mode and the traditional face-to-face classroom instruction should be combined together so as to improve the teaching effectiveness in modern education. Finally, how to blend the MOOC mode and the traditional classroom teaching and further utilize the network platform to improve the teaching efficiency in several learning phases are examined.

关键词： MOOCs face-to-face teaching traditional teaching network teaching platform

ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

学校读者我要写书评

暂无评论

Efficient Convolutional Forward Modeling and Sparse Coding i...

European Signal processing Conference (EUSIPCO)

作者： Han Wang Yhonatan Kvich Eduardo Pérez Florian Römer Yonina C. Eldar Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA).

关键词： Convolutional codes Adaptation models Convolution Computational modeling Signal processing algorithms Imaging Transforms Encoding Computational efficiency Sparse matrices

Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Han Kvich, Yhonatan Pérez, Eduardo Römer, Florian Eldar, Yonina C. Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany

关键词： Ultrasonic testing

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Chen, Zhaoliang Fu, Lele Yao, Jie Guo, Wenzhong Plant, Claudia Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria Research Network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

学校读者我要写书评

暂无评论

An Optimizing Parameters and Feature Selection in SVM Based ...

16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

Optimizing the numbers of queries and replies in federated learning with differential privacy

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhou, Yipeng Liu, Xuezheng Fu, Yao Wu, Di Li, Chao Yu, Shui The Department of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia The School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Co. Ltd China The School of Computer Science University of Technology Sydney Australia

Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can protect clients' raw data, malicious users can still crack original data with disclosed parameters. To amend this flaw, differential privacy (DP) is incorporated into FL clients to disturb original parameters, which however can significantly impair the accuracy of the trained model. In this work, we study a crucial question which has been vastly overlooked by existing works: what are the optimal numbers of queries and replies in FL with DP so that the final model accuracy is maximized. In FL, the parameter server (PS) needs to query participating clients for multiple global iterations to complete training. Each client responds a query from the PS by conducting a local iteration. Our work investigates how many times the PS should query clients and how many times each client should reply the PS. We investigate two most extensively used DP mechanisms (i.e., the Laplace mechanism and Gaussian mechanisms). Through conducting convergence rate analysis, we can determine the optimal numbers of queries and replies in FL with DP so that the final model accuracy can be maximized. Finally, extensive experiments are conducted with publicly available datasets: MNIST and FEMNIST, to verify our analysis and the results demonstrate that properly setting the numbers of queries and replies can significantly improve the final model accuracy in FL with DP. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning